CRAWDADFeed of newest CRAWDAD datasets and toolsimages/bullet.png2018-03-29T20:38:42.465964ZTristan Hendersoncrawdad@crawdad.orghttps://crawdad.org/coppe-ufrj/RioBuses (v. 2018-03-19)https://crawdad.org/coppe-ufrj/RioBuses/,2018:03:192018-03-19T00:00:00ZDaniel DiasLuís Henrique Maciel Kosmalski CostaReal-time position data reported by buses, updated every minute, from the city of Rio de Janeiro, Brazil. The file is CSV, containing the date, time(24h format), bus ID, bus line, latitude, longitude and speed of more than 12,000 buses.hasselt/EDM (v. 2017-06-09)https://crawdad.org/hasselt/EDM/,2017:06:092017-06-09T00:00:00ZPieter RobynsBram BonnPeter QuaxWim LamotteA complete collection of all management and control frames (including Radiotap headers) observed at our research lab from 28 January to 8 Febuary 2016. This dataset was used to calculate the "stability" and "variability" of Probe Request IEs (see our paper for more details on these metrics).hasselt/glimps2015 (v. 2017-05-11)https://crawdad.org/hasselt/glimps2015/,2017:05:112017-05-11T00:00:00ZPieter RobynsBram BonnéPeter QuaxWim LamotteA collection of 122,989 Probe Request frames captured by 8 monitoring stations at the Glimps music festival in Ghent, Belgium (10 - 12 December 2015). To minimize overhead, each monitoring station individually stored only the transmitter MAC address and Information Elements per unique MAC. The dataset was used to show that the high entropy in Information Elements can be used to deanonymize devices that use MAC address randomization.upb/hyccups (v. 2016-10-17)https://crawdad.org/upb/hyccups/,2016:10:172016-10-17T00:00:00ZRadu I. CiobanuCiprian DobreWireless contacts trace collected at the University Politehnica of Bucharest in the spring of 2012, using an application entitled HYCCUPS Tracer (http://hyccups.hpc.pub.ro), with the purpose of collecting contextual data from Android smartphones. It was run in the background and collected availability and mobile interaction information such as usage statistics, user activity, battery statistics, or sensor data, but it also gathered information about a device's encounters with other nodes or with wireless access points. Encounter collection was performed using AllJoyn. The data was collected by constructing and deleting wireless sessions using the AllJoyn framework based on WiFi. Tracing was executed asynchronously. The duration of the tracing experiment was 63 days, between March and May 2012, and had 72 participants, out of which only 42 had at least one contact. By analyzing the participants' Facebook profiles, the social connections matrix was extracted, as well as the users' interests. The trace (and others from the CRAWDAD collection) is parsed within the MobEmu simulator (used in all UPB's papers), publicly available at https://github.com/raduciobanu/mobemu.uoi/haggle (v. 2016-08-28)https://crawdad.org/uoi/haggle/,2016:08:282016-08-28T00:00:00ZDimitrios-Georgios AkestoridisThis dataset contains seven connectivity traces that have been derived from the cambridge/haggle/imote traceset (v. 2009-05-29). These connectivity traces can be used for network simulations with the Opportunistic Network Environment (ONE) simulator, since they are in accordance with the syntax of the StandardEventsReader format. The Python scripts that generated these connectivity traces are also provided.tools/simulate/uoi/adyton (v. 2016-04-21)https://crawdad.org/tools/simulate/uoi/adyton/,2016:04:212016-04-21T00:00:00ZNikolaos PapanikosDimitrios-Georgios AkestoridisEvangelos PapapetrouAdyton is an event-driven network simulator, written in C++, for Opportunistic Networks (a.k.a. Delay-Tolerant Networks) that is capable of processing contact traces. The Adyton simulator supports a plethora of routing protocols and real-world contact traces, while also providing several congestion control mechanisms and buffer management policies.oviedo/asturies-er (v. 2016-04-12)https://crawdad.org/oviedo/asturies-er/,2016:04:122016-04-12T00:00:00ZSergio CabreroRoberto GarciaXabiel G. GarciaDavid MelendiThis dataset contains ONE connectivity traces extracted from GPS traces collected from the regional Fire Department of Asturias, Spain. The original data source is one year of GPS traces extracted from a Geographical Information System (GIS). The traces were generated by GPS devices embedded mainly in cars and trucks, but also in a helicopter and a few personal radios. A total of 229 devices reported 19,462,339 locations. A new location is reported with an interval of approximately 30 seconds when the GPS device detects movement. To convert GPS traces into ONE connectivity traces, we have assumed circular communication ranges of 10, 50 and 200 meters. There is a connection between nodes that are closer than the given range. For simplicity, we assume that the position of a device is always the last position reported. Our analysis show several important findings for the design of network protocols from the physical to the application layer. The networks examined are heterogeneous in the contact duration and the number of nodes contacted (degree centrality). In addition, they are sparse and partitioned, but delay- tolerant routes connecting these partitions exist. Finally, there are patterns in the connection between nodes that can ease the discovery of these routes and the deployment of delay-tolerant services.copelabs/usense (v. 2016-03-17)https://crawdad.org/copelabs/usense/,2016:03:172016-03-17T00:00:00ZS. FirdoseL. LopesW. MoreiraR. SofiaP. MendesThis data set comprises experiments carried out considering four Android devices, each named Usense 2, 3, 4, and 5, respectively. These devices were carried by people sharing the same affiliation during their daily routines (commuting between home and office, going to leisure activities, attending meetings in the office). All the data was collected each and every one minute.buffalo/phonelab-wifi (v. 2016-03-09)https://crawdad.org/buffalo/phonelab-wifi/,2016:03:092016-03-09T00:00:00ZJinghao ShiChunming QiaoDimitrios KoutsonikolasGeoffrey ChallenSmartphones perform Wifi scans to adapt to the changing wireless environments causes by mobility. From network monitoring perspective, such scans provide a natural stream of network measurements from client's point of view. In order to see whether such measurements can provide new insights in monitoring large scale wireless networks, we collected the Wifi scan results data, together with other Wifi related logs, from the PhoneLab smartphone testbed over 5 months. All data are collected passively from the smartphones.uclouvain/mptcp_smartphone (v. 2016-03-04)https://crawdad.org/uclouvain/mptcp_smartphone/,2016:03:042016-03-04T00:00:00ZQuentin De ConinckMatthieu BaertsBenjamin HesmansOlivier BonaventureMultipath TCP is a recent TCP extension that enables multihomed hosts like smartphones to send and receive data over multiple interfaces. Despite the growing interest in this new TCP extension, little is known about its behavior with real applications in wireless networks. Our paper "A First Analysis of Multipath TCP on Smartphones" analyzes a trace from a SOCKS proxy serving smartphones using Multipath TCP. This first detailed study of real Multipath TCP smartphone traffic reveals several interesting points about its behavior in the wild. It confirms the heterogeneity of wireless and cellular networks which influences the scheduling of Multipath TCP. The analysis shows that most of the additional subflows are never used to send data. The amount of reinjections is also quantified and shows that they are not a major issue for the deployment of Multipath TCP. With our methodology to detect handovers, around a quarter of the connections using several subflows experience data handovers.